AI 中文总结
研究多状态关系状态网络推断问题，提出连续时间框架，通过状态依赖协变量驱动边在多状态间转换，针对不同采样方式给出有效推断方法，能准确恢复模型参数且计算高效，推广经典网络效应到多状态关系。
AI 中文摘要
关系状态指友谊或合作等概念，其关系会持续一段时间。目前多数方法局限于二元状态，而许多现实系统有多种关系状态。本文提出连续时间框架来建模和推断关系状态网络，边可在多个状态间转换。转换强度由状态依赖协变量驱动，可分解为锚定和拉动机制，有线性和非线性效应。针对全事件历史和面板数据两种采样方式给出方法，模拟研究和实证应用验证了方法有效性，该框架保留经典网络效应可解释性并推广到多状态关系。
英文摘要
Relational states refer to concepts such as friendship or collaboration, in which a relationship persists over a certain amount of time. Study of relational states often involves figuring out what factors contribute to the creation or dissolution of these relationships. However, most methods available now restrict their attention to binary states, i.e., ties that are either present or absent, even though many real-world systems evolve through multiple relational states (e.g., acquaintance, friendship, close friendship). We propose a continuous-time framework for modelling and inferring relational state networks in which each edge evolves by transitioning between two or more states. In our model, transition intensities are driven by state-dependent covariates that might be decomposed into anchoring (current-state) and pulling (target-state) mechanisms, with both linear and smooth non-linear effects. We address two common sampling regimes. With full event histories, a Cox-type partial likelihood with nested case-control sampling enables efficient estimation of both parametric and smooth effects. Instead, for panel data we derive a general ODE formulation for the likelihood, which leads to a particularly efficient inference procedure for binary state model. Simulation studies confirm accurate recovery of model parameters, and an empirical application to adolescent friendship data reproduces the substantive conclusions of established modelling techniques while offering substantial computational gains. The framework preserves the interpretability of classical network effects, generalizes them to multi-state ties, and scales to larger, more complex designs under both full-history and panel sampling designs.
